Add ML tutorials

.github/vale/styles/Vocab/OpenSearch/Products/accept.txt

@@ -8,6 +8,7 @@ Amazon SageMaker
 Ansible
 Auditbeat
 AWS Cloud
+Cohere Command
 Cognito
 Dashboards Query Language
 Data Prepper

_dashboards/dashboards-assistant/index.md

@@ -122,3 +122,4 @@ The following screenshot shows a saved conversation, along with actions you can
 
 - [Getting started guide for OpenSearch Assistant in OpenSearch Dashboards](https://github.com/opensearch-project/dashboards-assistant/blob/main/GETTING_STARTED_GUIDE.md)
 - [OpenSearch Assistant configuration through the REST API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/opensearch-assistant/)
+- [Build your own chatbot]({{site.url}}{{site.baseurl}}/ml-commons-plugin/tutorials/build-chatbot/)

_ml-commons-plugin/agents-tools/index.md

@@ -18,7 +18,7 @@ An _agent_ is a coordinator that uses a large language model (LLM) to solve a pr
 
 - [_Flow agent_](#flow-agents): Runs tools sequentially, in the order specified in its configuration. The workflow of a flow agent is fixed. Useful for retrieval-augmented generation (RAG).
 - [_Conversational flow agent_](#conversational-flow-agents): Runs tools sequentially, in the order specified in its configuration. The workflow of a conversational flow agent is fixed. Stores conversation history so that users can ask follow-up questions. Useful for creating a chatbot.
-- [_Conversational agent_](#conversational-agents): Reasons in order to provide a response based on the available knowledge, including the LLM knowledge base and a set of tools provided to the LLM. Stores conversation history so that users can ask follow-up questions. The workflow of a conversational agent is variable, based on follow-up questions. For specific questions, uses the Chain-of-Thought (CoT) process to select the best tool from the configured tools for providing a response to the question. Useful for creating a chatbot that employs RAG.
+- [_Conversational agent_](#conversational-agents): Reasons in order to provide a response based on the available knowledge, including the LLM knowledge base and a set of tools provided to the LLM. The LLM reasons iteratively to decide what action to take until it obtains the final answer or reaches the iteration limit. Stores conversation history so that users can ask follow-up questions. The workflow of a conversational agent is variable, based on follow-up questions. For specific questions, uses the Chain-of-Thought (CoT) process to select the best tool from the configured tools for providing a response to the question. Useful for creating a chatbot that employs RAG.
 
 ### Flow agents
 

_ml-commons-plugin/index.md

@@ -30,4 +30,8 @@ ML Commons supports various algorithms to help train ML models and make predicti
 
 ## ML Commons API
 
-ML Commons provides its own set of REST APIs. For more information, see [ML Commons API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/index/). 
+ML Commons provides its own set of REST APIs. For more information, see [ML Commons API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/index/). 
+
+## Tutorials
+
+Using the OpenSearch ML framework, you can build various applications, from implementing conversational search to building your own chatbot. For more information, see [Tutorials]({{site.url}}{{site.baseurl}}/ml-commons-plugin/tutorials/index/).

_ml-commons-plugin/tutorials/conversational-search-cohere.md

@@ -0,0 +1,228 @@
+---
+layout: default
+title: Conversational search with Cohere Command
+parent: Tutorials
+nav_order: 20
+---
+
+# Conversational search using the Cohere Command model
+
+This tutorial illustrates how to configure conversational search using the Cohere Command model. For more information, see [Conversational search]({{site.url}}{{site.baseurl}}/search-plugins/conversational-search/).
+
+Replace the placeholders beginning with the prefix `your_` with your own values.
+{: .note}
+
+Alternatively, you can build a RAG/conversational search using agents and tools. For more information, see [Retrieval-augmented generation chatbot]({{site.url}}{{site.baseurl}}/ml-commons-plugin/tutorials/rag-conversational-agent/).
+
+## Prerequisite
+
+Ingest test data:
+
+```json
+POST _bulk
+{"index": {"_index": "qa_demo", "_id": "1"}}
+{"text": "Chart and table of population level and growth rate for the Ogden-Layton metro area from 1950 to 2023. United Nations population projections are also included through the year 2035.\nThe current metro area population of Ogden-Layton in 2023 is 750,000, a 1.63% increase from 2022.\nThe metro area population of Ogden-Layton in 2022 was 738,000, a 1.79% increase from 2021.\nThe metro area population of Ogden-Layton in 2021 was 725,000, a 1.97% increase from 2020.\nThe metro area population of Ogden-Layton in 2020 was 711,000, a 2.16% increase from 2019."}
+{"index": {"_index": "qa_demo", "_id": "2"}}
+{"text": "Chart and table of population level and growth rate for the New York City metro area from 1950 to 2023. United Nations population projections are also included through the year 2035.\\nThe current metro area population of New York City in 2023 is 18,937,000, a 0.37% increase from 2022.\\nThe metro area population of New York City in 2022 was 18,867,000, a 0.23% increase from 2021.\\nThe metro area population of New York City in 2021 was 18,823,000, a 0.1% increase from 2020.\\nThe metro area population of New York City in 2020 was 18,804,000, a 0.01% decline from 2019."}
+{"index": {"_index": "qa_demo", "_id": "3"}}
+{"text": "Chart and table of population level and growth rate for the Chicago metro area from 1950 to 2023. United Nations population projections are also included through the year 2035.\\nThe current metro area population of Chicago in 2023 is 8,937,000, a 0.4% increase from 2022.\\nThe metro area population of Chicago in 2022 was 8,901,000, a 0.27% increase from 2021.\\nThe metro area population of Chicago in 2021 was 8,877,000, a 0.14% increase from 2020.\\nThe metro area population of Chicago in 2020 was 8,865,000, a 0.03% increase from 2019."}
+{"index": {"_index": "qa_demo", "_id": "4"}}
+{"text": "Chart and table of population level and growth rate for the Miami metro area from 1950 to 2023. United Nations population projections are also included through the year 2035.\\nThe current metro area population of Miami in 2023 is 6,265,000, a 0.8% increase from 2022.\\nThe metro area population of Miami in 2022 was 6,215,000, a 0.78% increase from 2021.\\nThe metro area population of Miami in 2021 was 6,167,000, a 0.74% increase from 2020.\\nThe metro area population of Miami in 2020 was 6,122,000, a 0.71% increase from 2019."}
+{"index": {"_index": "qa_demo", "_id": "5"}}
+{"text": "Chart and table of population level and growth rate for the Austin metro area from 1950 to 2023. United Nations population projections are also included through the year 2035.\\nThe current metro area population of Austin in 2023 is 2,228,000, a 2.39% increase from 2022.\\nThe metro area population of Austin in 2022 was 2,176,000, a 2.79% increase from 2021.\\nThe metro area population of Austin in 2021 was 2,117,000, a 3.12% increase from 2020.\\nThe metro area population of Austin in 2020 was 2,053,000, a 3.43% increase from 2019."}
+{"index": {"_index": "qa_demo", "_id": "6"}}
+{"text": "Chart and table of population level and growth rate for the Seattle metro area from 1950 to 2023. United Nations population projections are also included through the year 2035.\\nThe current metro area population of Seattle in 2023 is 3,519,000, a 0.86% increase from 2022.\\nThe metro area population of Seattle in 2022 was 3,489,000, a 0.81% increase from 2021.\\nThe metro area population of Seattle in 2021 was 3,461,000, a 0.82% increase from 2020.\\nThe metro area population of Seattle in 2020 was 3,433,000, a 0.79% increase from 2019."}
+```
+{% include copy-curl.html %}
+
+## Step 1: Create a connector and register a model
+
+Conversational search only supports the [OpenAI](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/open_ai_connector_chat_blueprint.md) 
+and [Amazon Bedrock Claude](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/bedrock_connector_anthropic_claude_blueprint.md) input/output styles.
+{: .important}
+
+This tutorial follows the Amazon Bedrock Claude model input/output style by:
+- Mapping the Cohere Command `message` input parameter to the `inputs` parameter in order to match the Cohere Claude model input style.
+- Using a post-processing function to convert the Cohere Command model output to the Claude model output style.
+
+Create a connector for the Cohere Command model:
+
+```json
+POST _plugins/_ml/connectors/_create
+{
+    "name": "Cohere Chat Model",
+    "description": "The connector to Cohere's public chat API",
+    "version": "1",
+    "protocol": "http",
+    "credential": {
+        "cohere_key": "your_cohere_api_key"
+    },
+    "parameters": {
+        "model": "command"
+    },
+    "actions": [
+        {
+            "action_type": "predict",
+            "method": "POST",
+            "url": "https://api.cohere.ai/v1/chat",
+            "headers": {
+                "Authorization": "Bearer ${credential.cohere_key}",
+                "Request-Source": "unspecified:opensearch"
+            },
+            "request_body": "{ \"message\": \"${parameters.inputs}\", \"model\": \"${parameters.model}\" }",
+            "post_process_function": "\n    String escape(def input) { \n      if (input.contains(\"\\\\\")) {\n        input = input.replace(\"\\\\\", \"\\\\\\\\\");\n      }\n      if (input.contains(\"\\\"\")) {\n        input = input.replace(\"\\\"\", \"\\\\\\\"\");\n      }\n      if (input.contains('\r')) {\n        input = input = input.replace('\r', '\\\\r');\n      }\n      if (input.contains(\"\\\\t\")) {\n        input = input.replace(\"\\\\t\", \"\\\\\\\\\\\\t\");\n      }\n      if (input.contains('\n')) {\n        input = input.replace('\n', '\\\\n');\n      }\n      if (input.contains('\b')) {\n        input = input.replace('\b', '\\\\b');\n      }\n      if (input.contains('\f')) {\n        input = input.replace('\f', '\\\\f');\n      }\n      return input;\n    }\n    def name = 'response';\n    def result = params.text;\n    def json = '{ \"name\": \"' + name + '\",' +\n          '\"dataAsMap\": { \"completion\":  \"' + escape(result) +\n          '\"}}';\n    return json;\n   \n    "
+        }
+    ]
+}
+```
+{% include copy-curl.html %}
+
+Starting in OpenSearch 2.12, you can use the default `escape` function directly in the `post_process_function`:
+
+```json
+"post_process_function": "    \n    def name = 'response';\n    def result = params.text;\n    def json = '{ \"name\": \"' + name + '\",' +\n                 '\"dataAsMap\": { \"completion\":  \"' + escape(result) +\n               '\"}}';\n    return json;"
+```
+{% include copy-curl.html %}
+
+Note the connector ID; you'll use it to register the model.
+
+Register the Cohere Command model:
+
+```json
+POST /_plugins/_ml/models/_register?deploy=true
+{
+    "name": "Cohere command model",
+    "function_name": "remote",
+    "description": "Cohere command model",
+    "connector_id": "your_connector_id"
+}
+```
+{% include copy-curl.html %}
+
+Note the model ID; you'll use it in the following steps.
+
+Test the model:
+
+```json
+POST /_plugins/_ml/models/your_model_id/_predict
+{
+  "parameters": {
+    "inputs": "What is the weather like in Seattle?"
+  }
+}
+```
+{% include copy-curl.html %}
+
+The response contains the LLM completion:
+
+```json
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "response",
+          "dataAsMap": {
+            "completion": """It is difficult to provide a comprehensive answer without a specific location or time frame in mind. 
+
+As an AI language model, I have no access to real-time data or the ability to provide live weather reports. Instead, I can offer some general information about Seattle's weather, which is known for its mild, wet climate. 
+
+Located in the Pacific Northwest region of the United States, Seattle experiences a maritime climate with cool, dry summers and mild, wet winters. While it is best known for its rainy days, Seattle's annual rainfall is actually less than New York City and Boston. 
+
+Would you like me to provide more details on Seattle's weather?  Or, if you have a specific date or location in mind, I can try to retrieve real-time or historical weather information for you."""
+          }
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```
+
+## Step 2: Configure conversational search
+
+Create a search pipeline containing a RAG processor:
+
+```json
+PUT /_search/pipeline/my-conversation-search-pipeline-cohere
+{
+  "response_processors": [
+    {
+      "retrieval_augmented_generation": {
+        "tag": "Demo pipeline",
+        "description": "Demo pipeline Using Cohere",
+        "model_id": "your_model_id_created_in_step1",
+        "context_field_list": [
+          "text"
+        ],
+        "system_prompt": "You are a helpful assistant",
+        "user_instructions": "Generate a concise and informative answer in less than 100 words for the given question"
+      }
+    }
+  ]
+}
+```
+{% include copy-curl.html %}
+
+To run a conversational search, specify its parameters in the `generative_qa_parameters` object:
+
+```json
+GET /qa_demo/_search?search_pipeline=my-conversation-search-pipeline-cohere
+{
+  "query": {
+    "match": {
+      "text": "What's the population increase of New York City from 2021 to 2023?"
+    }
+  },
+  "size": 1,
+  "_source": [
+    "text"
+  ],
+  "ext": {
+    "generative_qa_parameters": {
+      "llm_model": "bedrock/claude",
+      "llm_question": "What's the population increase of New York City from 2021 to 2023?",
+      "context_size": 5,
+      "timeout": 15
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+The response contains the model's answer:
+
+```json
+{
+  "took": 1,
+  "timed_out": false,
+  "_shards": {
+    "total": 1,
+    "successful": 1,
+    "skipped": 0,
+    "failed": 0
+  },
+  "hits": {
+    "total": {
+      "value": 6,
+      "relation": "eq"
+    },
+    "max_score": 9.042081,
+    "hits": [
+      {
+        "_index": "qa_demo",
+        "_id": "2",
+        "_score": 9.042081,
+        "_source": {
+          "text": """Chart and table of population level and growth rate for the New York City metro area from 1950 to 2023. United Nations population projections are also included through the year 2035.\nThe current metro area population of New York City in 2023 is 18,937,000, a 0.37% increase from 2022.\nThe metro area population of New York City in 2022 was 18,867,000, a 0.23% increase from 2021.\nThe metro area population of New York City in 2021 was 18,823,000, a 0.1% increase from 2020.\nThe metro area population of New York City in 2020 was 18,804,000, a 0.01% decline from 2019."""
+        }
+      }
+    ]
+  },
+  "ext": {
+    "retrieval_augmented_generation": {
+      "answer": "The population of the New York City metro area increased by about 210,000 from 2021 to 2023. The 2021 population was 18,823,000, and in 2023 it was 18,937,000. The average growth rate is 0.23% yearly."
+    }
+  }
+}
+```